Experience API recipes/visualization services are free for education institutions, partners are welcome
Contact: mlearning@classroomaid.org
See detailed features list:
http://classroom-aid.com/xapi-and-analytics-services/
Across System Learning Environment and Dashboard Design for K12 Teachers and ...Jessie Chuang
Implementing xAPI in several educational technologies:
Flipped learning platform - 1Know
IRS and assessment - HDTEDU
eBook readers - Delta
Google Apps for Education - MiTAC
Mobile Learning Apps - Claro
Goals:
Helping Chinese CoP members implement xAPI, and showcasing Taiwan vendors and the integrated services
Across System Learning Environment and Dashboard Design for 350,000+ K12 teachers and students
Building the foundation for extensible learning data analytics, functionalities, and services developed/offered by 3rd parties
X api chinese cop monthly meeting feb.2016Jessie Chuang
Topics
XAPI Vocabulary spec. From ADL
Linked Data / Semantic web. / Web 3.0
Linked Data in education and content recommender
Semantic search and Google Knowledge Graph
APIs eat software (connect with partners and services)
How should we exploit data and build intelligence layer?
Case Study (Hong Ding Educational Technology)
Monetize your data and add value (intelligence)
IEEE TAG xAPI Webinar Series: Improving the Learner Experience Through an xAP...Margaret Roth
As part of the xAPI Case Studies Webinar Series hosted by the IEEE LTSC TAG xAPI, this presentation gives an overview of the creation of the Learning Commons. The Learning Commons is a shared space for connecting and accelerating educator learning created in partnership by the Learning Accelerator and Yet Analytics with support from innovative education organizations. The Learning Commons is an xAPI-enabled, multi-source content portal designed from the ground up with xAPI as part of the data model, powering a unified learner experience interface that allows learners to see how the informal learning content they work with aligns to competencies and skills they are working to build. As learners utilize content through curated playlists, skill and competency development is automatically tracked and presented back to learners, cohort leaders, and content providers through xAPI data.
The Google Slides version can be accessed at http://goog.gl/HfuscA. This presentation was presented in the xAPI Case Studies Webinar Series hosted by the IEEE LTSC TAG xAPI group on April 17, 2018.
Across System Learning Environment and Dashboard Design for K12 Teachers and ...Jessie Chuang
Implementing xAPI in several educational technologies:
Flipped learning platform - 1Know
IRS and assessment - HDTEDU
eBook readers - Delta
Google Apps for Education - MiTAC
Mobile Learning Apps - Claro
Goals:
Helping Chinese CoP members implement xAPI, and showcasing Taiwan vendors and the integrated services
Across System Learning Environment and Dashboard Design for 350,000+ K12 teachers and students
Building the foundation for extensible learning data analytics, functionalities, and services developed/offered by 3rd parties
X api chinese cop monthly meeting feb.2016Jessie Chuang
Topics
XAPI Vocabulary spec. From ADL
Linked Data / Semantic web. / Web 3.0
Linked Data in education and content recommender
Semantic search and Google Knowledge Graph
APIs eat software (connect with partners and services)
How should we exploit data and build intelligence layer?
Case Study (Hong Ding Educational Technology)
Monetize your data and add value (intelligence)
IEEE TAG xAPI Webinar Series: Improving the Learner Experience Through an xAP...Margaret Roth
As part of the xAPI Case Studies Webinar Series hosted by the IEEE LTSC TAG xAPI, this presentation gives an overview of the creation of the Learning Commons. The Learning Commons is a shared space for connecting and accelerating educator learning created in partnership by the Learning Accelerator and Yet Analytics with support from innovative education organizations. The Learning Commons is an xAPI-enabled, multi-source content portal designed from the ground up with xAPI as part of the data model, powering a unified learner experience interface that allows learners to see how the informal learning content they work with aligns to competencies and skills they are working to build. As learners utilize content through curated playlists, skill and competency development is automatically tracked and presented back to learners, cohort leaders, and content providers through xAPI data.
The Google Slides version can be accessed at http://goog.gl/HfuscA. This presentation was presented in the xAPI Case Studies Webinar Series hosted by the IEEE LTSC TAG xAPI group on April 17, 2018.
Modern Learning Ecosystem Design with xAPIMargaret Roth
While the L&D community is increasingly familiar with the Experience API (xAPI) and its value for data collection and interoperability, few examples exist to clarify the value of xAPI as applied within different existing learning infrastructures. This session focused on sharing the ways xAPI can connect and provide value in any eLearning environment.
These slides present a series of different learning ecosystem configurations and the ways xAPI and a learning record store (LRS) can provide value in each case. The three main learning ecosystem configurations examined range from the simplest (LMS and LRS) to three systems connected (LMS, LRS, and CMS) to the fully modular (LRS, LMS, simulations, microlearning, performance assessment, and other tools). For each of these configurations, the presentation shares specific values and practical applications gained by connecting an xAPI LRS to the existing system.
This presentation was originally shared as part of the eLearning Guild's 2018 Learning Solutions conference on March 28, 2018.
How to Plan for an xAPI Pilot at xAPI Camp DevLearn 2018 - Yet AnalyticsAllie Tscheulin
From an organization-wide executive directive to become more data-driven, a retail corporate L&D team took an internal look at their own data practices. Realizing that they had an overwhelming lack of transparency into their learning initiatives and a great amount of data that had gone unused, the team developed a transformation vision to create a single system of record for learning to enable observability, granularity, and accountability for all team members. The team was committed to the vision of xAPI; however, the data and information they needed in order to make actionable change for their learners was locked away in non-interoperable formats, and they recognized the need to develop a data strategy and implementation plan.
*Originally presented on 10/ 23/2018 at xAPI Camp during DevLearn 2018 by Allie Tscheulin
DESCRIPTION:
Learning analytics is at a critical juncture in its lifecycle. To date, much of the learning analytics-related research, software development, and standards work that exists has taken place in relative isolation. This lack of collaboration, openness, and integrated systems greatly limits the
potential of learning analytics. LA initiatives have typically been dependent upon “closed” systems, proprietary data models and single use tools – as opposed to an integrated software suite for analyzing and communicating data on learning processes.
As institutions begin to move past discussion and into implementation of learning analytics environments, the realization of an open source platform for learning analytics becomes increasingly important as an option for institutions to consider alongside commercial offerings. In this presentation, learning analytics practitioners Josh Baron, Sandeep Jayaprakash and Alan Berg discuss a strategic vision of an open source platform, including standards, systems, and tools, that can lower the barrier to entry for institutions looking to get started with learning analytics.
There will be a short demo of current components of the platform as well as details on accessing/contributing to the open-source code repository and how to get more involved in the Apereo LAI.
Currently Experience API (xAPI) mostly focuses on providing “structural” interoperability of xAPI statements via JavaScript Object Notation Language (JSON). Structural interoperability defines the syntax of the data exchange and ensures the data exchanged between systems can be interpreted at the data field level. In comparison, semantic interoperability leverages the structural interoperability of the data exchange, but provides a vocabulary so other systems and consumers can also interpret the data. Analytics produced by xAPI statements would benefit from more consistent and semantic approaches to describing domain-specific verbs, activityTypes, attachments, and extensions. The xAPI specification recommends implementers to adopt community-defined vocabularies, but the only current guidance is to provide very basic, human-readable identifier metadata (e.g., literal string name(display), description). The main objective of the Vocabulary and Semantic Interoperability Working Group (WG) is to research machine-readable, semantic technologies (e.g., RDF, JSON-LD) in order to produce guidance for Communities of Practice (CoPs) on creating, publishing, or managing controlled vocabulary datasets (e.g., verbs). In this session, you will see a brief introduction to modern controlled vocabulary practices and how they can be applied to xAPI to add semantic expressiveness of controlled vocabularies. The progress and resources from the Vocabulary WG (started in April 2015) will also be shared.
Combining content analytics and activity tracking to mine user interests and ...Andrii Vozniuk
The paper was presented at UMAP PALE 2016: goo.gl/5cJsSK
Finding relevant content is one of the core activities of users interacting with a content repository, be it knowledge workers using an organizational knowledge management system at a workplace or self-regulated learners collaborating in a learning environment. Due to the number of content items stored in such repositories potentially reaching millions or more, and quickly increasing, for the user it can be challenging to find relevant content by browsing or relying on the available search engine. In this paper, we propose to address the problem by providing content and people recommendations based on user interests, enabling relevant knowledge discovery. To build a user interests profile automatically, we propose an approach combining content analytics and activity tracking. We have implemented the recommender system in Graasp, a knowledge management system employed in educational and humanitarian domains. The conducted preliminary evaluation demonstrated an ability of the approach to identify interests relevant to the user and to recommend relevant content.
A Pulse of Predictive Analytics In Higher Education │ Civitas LearningCivitas Learning
Civitas Learning presents the findings of our survey conducted during the September 2014 Civitas Learning Summit, where more than 100 leaders representing 40 Pioneer Partner institutions gathered to share more on their work. The survey, distributed to all participants, resulted in 74 responses highlighting how this cross-section of higher education institutions are using advanced analytics to power student success initiatives.
Materials for introduction to adaptive learning and learning analytics as well as efforts of interoperability standardization. This slides treats brief concept of adaptive learning, reference model of learning analytics, data APIs for learning analytics, and topic list of standardization community (ISO/IEC JTC1 SC36).
This slide was presented in International the 2015 Conference on Education Research.
I aggregated several my other partial slides and reports to describe adaptive learning model pertaining to concept of learning analytics as well as LOD for curriculum standards and digital resources. There is short introduction to the project of ISO/IEC 20748 Learning analytics interoperability - Part 1: Reference model.
What does-x api-mean-for-your-learning-data and analytics-strategy-slideshareJames Stack
An introductory presentation on learning analytics. It includes a learning analytics maturity model and a schema for thinking about xAPI, data modelling, xAPI activity providers and how analytics should inform continuous improvement.
Patterns for New Software Engineering: Machine Learning and IoT Engineering P...Hironori Washizaki
Hironori Washizaki, "Patterns for New Software Engineering: Machine Learning and IoT Engineering Patterns", Keynote, AsianPLoP 2020: 9th Asian Conference on Pattern Languages of Programs, Sep 3rd, 2020.
2 September - 4 September, 2020
Jack Buckley, Commissioner for the National Center for Education Statistics, presented at the Content in Context Metadata Lab on the work the U.S. Department of Education has done on the Common Education Data Standards.
Scalable Learning Analytics and Interoperability – an assessment of potential...LACE Project
A presentation given at the 2015 EUNIS Congress, held at Abertay University in Dundee, June 2015.
Learning analytics is now moving from being a research interest to topic for adoption. As this happens, the challenge of efficiently and reliably moving data between systems becomes of vital practical importance. In this context, “scalable learning analytics” is not intended to refer to infrastructural throughput, but to refer to the feasibility of a combination of: a) pervasive system
integration, and b) efficient analytical and data management practices. There are a number of
considerations that are of particular relevance to learning analytics in addition to elements that are generic to analytics. This contribution to EUNIS 2015 seeks to clarify, by argument and through evidence, both where there are potential benefits and limitations to applying interoperability specifications (and standards) in the service of scalable learning analytics.
In this webinar, Andrew Downes will run through nine practical Tin Can API (xAPI) use cases that you can begin working on today. For each use case, he’ll explain the benefits to your organization, and then outline a step-by-step plan you can follow to pilot that use case. You’ll learn what you need to ask your existing vendors, what you need to buy, and what you need to build; everything you need to know to get started.
What use cases will you learn about?
* Learning Analytics
* Better Blended Learning
* Adaptive Pathways
* Just-in-Time Performance Support
* Mentoring
* Team Learning
* Multi-device Learning
* LRS to LRS communication
* Open Badges
XAPI and Machine Learning for Patient / LearnerJessie Chuang
xAPI and Machine Learning can help us build "intelligent assistance" for patients and learners, but human-in-the-loop machine learning is important. We need good learning design from the beginning and as we return data to instructors and learners immediately, humans can give great inputs to this human-machine collaboration.
Modern Learning Ecosystem Design with xAPIMargaret Roth
While the L&D community is increasingly familiar with the Experience API (xAPI) and its value for data collection and interoperability, few examples exist to clarify the value of xAPI as applied within different existing learning infrastructures. This session focused on sharing the ways xAPI can connect and provide value in any eLearning environment.
These slides present a series of different learning ecosystem configurations and the ways xAPI and a learning record store (LRS) can provide value in each case. The three main learning ecosystem configurations examined range from the simplest (LMS and LRS) to three systems connected (LMS, LRS, and CMS) to the fully modular (LRS, LMS, simulations, microlearning, performance assessment, and other tools). For each of these configurations, the presentation shares specific values and practical applications gained by connecting an xAPI LRS to the existing system.
This presentation was originally shared as part of the eLearning Guild's 2018 Learning Solutions conference on March 28, 2018.
How to Plan for an xAPI Pilot at xAPI Camp DevLearn 2018 - Yet AnalyticsAllie Tscheulin
From an organization-wide executive directive to become more data-driven, a retail corporate L&D team took an internal look at their own data practices. Realizing that they had an overwhelming lack of transparency into their learning initiatives and a great amount of data that had gone unused, the team developed a transformation vision to create a single system of record for learning to enable observability, granularity, and accountability for all team members. The team was committed to the vision of xAPI; however, the data and information they needed in order to make actionable change for their learners was locked away in non-interoperable formats, and they recognized the need to develop a data strategy and implementation plan.
*Originally presented on 10/ 23/2018 at xAPI Camp during DevLearn 2018 by Allie Tscheulin
DESCRIPTION:
Learning analytics is at a critical juncture in its lifecycle. To date, much of the learning analytics-related research, software development, and standards work that exists has taken place in relative isolation. This lack of collaboration, openness, and integrated systems greatly limits the
potential of learning analytics. LA initiatives have typically been dependent upon “closed” systems, proprietary data models and single use tools – as opposed to an integrated software suite for analyzing and communicating data on learning processes.
As institutions begin to move past discussion and into implementation of learning analytics environments, the realization of an open source platform for learning analytics becomes increasingly important as an option for institutions to consider alongside commercial offerings. In this presentation, learning analytics practitioners Josh Baron, Sandeep Jayaprakash and Alan Berg discuss a strategic vision of an open source platform, including standards, systems, and tools, that can lower the barrier to entry for institutions looking to get started with learning analytics.
There will be a short demo of current components of the platform as well as details on accessing/contributing to the open-source code repository and how to get more involved in the Apereo LAI.
Currently Experience API (xAPI) mostly focuses on providing “structural” interoperability of xAPI statements via JavaScript Object Notation Language (JSON). Structural interoperability defines the syntax of the data exchange and ensures the data exchanged between systems can be interpreted at the data field level. In comparison, semantic interoperability leverages the structural interoperability of the data exchange, but provides a vocabulary so other systems and consumers can also interpret the data. Analytics produced by xAPI statements would benefit from more consistent and semantic approaches to describing domain-specific verbs, activityTypes, attachments, and extensions. The xAPI specification recommends implementers to adopt community-defined vocabularies, but the only current guidance is to provide very basic, human-readable identifier metadata (e.g., literal string name(display), description). The main objective of the Vocabulary and Semantic Interoperability Working Group (WG) is to research machine-readable, semantic technologies (e.g., RDF, JSON-LD) in order to produce guidance for Communities of Practice (CoPs) on creating, publishing, or managing controlled vocabulary datasets (e.g., verbs). In this session, you will see a brief introduction to modern controlled vocabulary practices and how they can be applied to xAPI to add semantic expressiveness of controlled vocabularies. The progress and resources from the Vocabulary WG (started in April 2015) will also be shared.
Combining content analytics and activity tracking to mine user interests and ...Andrii Vozniuk
The paper was presented at UMAP PALE 2016: goo.gl/5cJsSK
Finding relevant content is one of the core activities of users interacting with a content repository, be it knowledge workers using an organizational knowledge management system at a workplace or self-regulated learners collaborating in a learning environment. Due to the number of content items stored in such repositories potentially reaching millions or more, and quickly increasing, for the user it can be challenging to find relevant content by browsing or relying on the available search engine. In this paper, we propose to address the problem by providing content and people recommendations based on user interests, enabling relevant knowledge discovery. To build a user interests profile automatically, we propose an approach combining content analytics and activity tracking. We have implemented the recommender system in Graasp, a knowledge management system employed in educational and humanitarian domains. The conducted preliminary evaluation demonstrated an ability of the approach to identify interests relevant to the user and to recommend relevant content.
A Pulse of Predictive Analytics In Higher Education │ Civitas LearningCivitas Learning
Civitas Learning presents the findings of our survey conducted during the September 2014 Civitas Learning Summit, where more than 100 leaders representing 40 Pioneer Partner institutions gathered to share more on their work. The survey, distributed to all participants, resulted in 74 responses highlighting how this cross-section of higher education institutions are using advanced analytics to power student success initiatives.
Materials for introduction to adaptive learning and learning analytics as well as efforts of interoperability standardization. This slides treats brief concept of adaptive learning, reference model of learning analytics, data APIs for learning analytics, and topic list of standardization community (ISO/IEC JTC1 SC36).
This slide was presented in International the 2015 Conference on Education Research.
I aggregated several my other partial slides and reports to describe adaptive learning model pertaining to concept of learning analytics as well as LOD for curriculum standards and digital resources. There is short introduction to the project of ISO/IEC 20748 Learning analytics interoperability - Part 1: Reference model.
What does-x api-mean-for-your-learning-data and analytics-strategy-slideshareJames Stack
An introductory presentation on learning analytics. It includes a learning analytics maturity model and a schema for thinking about xAPI, data modelling, xAPI activity providers and how analytics should inform continuous improvement.
Patterns for New Software Engineering: Machine Learning and IoT Engineering P...Hironori Washizaki
Hironori Washizaki, "Patterns for New Software Engineering: Machine Learning and IoT Engineering Patterns", Keynote, AsianPLoP 2020: 9th Asian Conference on Pattern Languages of Programs, Sep 3rd, 2020.
2 September - 4 September, 2020
Jack Buckley, Commissioner for the National Center for Education Statistics, presented at the Content in Context Metadata Lab on the work the U.S. Department of Education has done on the Common Education Data Standards.
Scalable Learning Analytics and Interoperability – an assessment of potential...LACE Project
A presentation given at the 2015 EUNIS Congress, held at Abertay University in Dundee, June 2015.
Learning analytics is now moving from being a research interest to topic for adoption. As this happens, the challenge of efficiently and reliably moving data between systems becomes of vital practical importance. In this context, “scalable learning analytics” is not intended to refer to infrastructural throughput, but to refer to the feasibility of a combination of: a) pervasive system
integration, and b) efficient analytical and data management practices. There are a number of
considerations that are of particular relevance to learning analytics in addition to elements that are generic to analytics. This contribution to EUNIS 2015 seeks to clarify, by argument and through evidence, both where there are potential benefits and limitations to applying interoperability specifications (and standards) in the service of scalable learning analytics.
In this webinar, Andrew Downes will run through nine practical Tin Can API (xAPI) use cases that you can begin working on today. For each use case, he’ll explain the benefits to your organization, and then outline a step-by-step plan you can follow to pilot that use case. You’ll learn what you need to ask your existing vendors, what you need to buy, and what you need to build; everything you need to know to get started.
What use cases will you learn about?
* Learning Analytics
* Better Blended Learning
* Adaptive Pathways
* Just-in-Time Performance Support
* Mentoring
* Team Learning
* Multi-device Learning
* LRS to LRS communication
* Open Badges
XAPI and Machine Learning for Patient / LearnerJessie Chuang
xAPI and Machine Learning can help us build "intelligent assistance" for patients and learners, but human-in-the-loop machine learning is important. We need good learning design from the beginning and as we return data to instructors and learners immediately, humans can give great inputs to this human-machine collaboration.
xAPI Making Sense of Industry and PracticeAaron Silvers
An overview of questions @MeganBowe and I recommend asking when considering your first big project with xAPI, and how the consortium that will steward xAPI will make this easier.
First delivered as a Learning Solutions 'Data and Measurement' Track Conference Session on March 22, 2017 by Janet Laane-Effron and Sean Putman.
Find out more about HT2 Labs' research and development at HT2Labs.com
xAPI in Action: Sending an data to an LRS (FocusOn Session)Jeff Batt
Here are my slides of how to send xAPI (TinCan) statements over to an LRS. This is a brief overview of the concept and then download files have walk through examples.
The Impacts of the Tin Can API: How 8 Companies are Using the Tin Can API (xAPI)Rustici Software
The Tin Can API is having major impacts on the direction of the e-learning industry.
Organizations and vendors of various types are rushing to adopt Tin Can because it enables many things they have wanted to do for a long time. Things like mobile delivery, offline delivery, serious games and hosting content outside the LMS were all difficult or impossible with SCORM. These are easy with Tin Can.
This webinar lets you get an in-depth look at what Tin Can means to various types of software and organizations, and learn what you need to be doing to make sure that you're keeping up with the trends that Tin Can has enabled in our industry. It features eight companies, each of which will tell you how they're using the Tin Can API, and what it means for their business.
From our xAPI Camp at Amazon's Headquarters in Seattle, WA on July 21, 2015. The decision to go with xAPI is an exciting one, but a successful xAPI project hinges on an understanding of what success looks like. In this presentation, I share a number of questions one should ask of technology partners and your own team depending on different ways one might use xAPI.
The Business Case for Adopting Tin Can (xAPI) - Why and How Five Product Vend...Rustici Software
Many of our previous webinars have given general information about Tin Can or focused specifically on how organizations can adopt. If you’re a product vendor, this next webinar is specifically for you. You’ll hear the stories of five learning product vendors that made the decision to adopt, implement Tin Can in their products, and roll it out to their customers.
If you’re not sure whether you should adopt, or you’re struggling to make the business case within your company, then this webinar will be very helpful for you.
You’ll hear from the following vendors:
*Cognitive Advisors
*gomo
*TES
*Tribridge
*Unicorn
When building software, the API first approach is the only way to go. Enterprises that are seeking Software Development must definitely consider leveraging the unlimited potential of building APIs.
How H2020 European ProjexAct is proposing to use xAPI for doing gaming learning analytics (GLA) with serios games. It describes the full approach from research to models, to methodologies to GLA supporting software to examples.
A quick history of my experience of eLearning and a look at current industry trends. Presentation for CUNA (Credit Union National Association) on October 27, 2015.
How to build an API your developers will love - Code.talks 2015, Hamburg by M...Michael Kuehne-Schlinkert
In the last years API spread out around the world. Every modern application provides or consumes at least one API. It became very easy to setup an API, but it became even easier to build APIs no one really wanted to use. Especially if you are providing a public API, your API should be so user friendly that your mom could use it or at least an inexperienced developer who never used an API before. As an independent software engineer I have worked with various clients designing, building, testing, maintaining and even redesigning private and public APIs; starting from a simple API for Single-Page-Application to a highly scalable and complex kickass API serving millions of users every day.
In this talk I would like to share my experience from these projects giving some guidelines on API design and answering some questions which occur in every API project like "How can we provide this operation and still being RESTful?" or "How should we version our API?" Besides an admirable API Design Testing, Documentation and Mocking are always sticking points, which I want to demystify by sharing my handy approach. With this talk I want to inspire you to apply some of these techniques in your next API project.
Preparing for Next Generation eLearning - Part I - Responsive eLearning & Tin...Upside Learning Solutions
Presentation discusses the challenges and opportunities that organisations are facing in moving to the next generation of eLearning. We discuss Responsive eLearning & Tin Can in Part I.
A brief history of eLearning as seen through the lens of my own personal experience. A look at current trends we're seeing that influence how we design and deliver online learning programs. Presented at ATD Tech Knowledge, January 14, 2016.
What shade of instructional designer are you? How can you focus your practice and refine your shade? Session slides from an eLearning Guild Online Forum on January 20, 2016.
Yet LXi — Learning Experience Interface Overview Margaret Roth
Yet’s Learning Experience Interface (LXi) enables the collection and tagging of resources across any source on the internet, providing a unified discovery and experience platform for informal, self-directed learning. Related content suggestions and a fully xAPI instrumented interface make the Yet LXi the best way to unify both your learner experience and your learning analytics.
This presentation was originally shared as part of the eThink Partner Webinar series on April 25, 2018. View the webinar recording at https://www.youtube.com/watch?v=rgxSEO-x2co&feature=youtu.be.
Education must capitalize on the trend within technology toward big data. New types of data are becoming available. From evidence approaches to xAPI and the whole Training and Learning Architecture(TLA) big data is the foundation of all.
The Connecticut Distance Learning Consortium (CTDLC) provides eLearning services and support for multiple platforms including Angel, Blackboard, and Moodle for K-20+ institutions. The CTDLC will review how it assists institutions in evaluating which LMS product is appropriate for their current and forecasted needs.
A Business User's Guide to Getting Started with xAPIMargaret Roth
This session was presented at the 2018 Association for Talent Development's TechKnowledge Conference in San Jose, CA.
The Experience API (xAPI) and the learning record store (LRS) are powerful tools for any learning organization, providing improved visibility into learning data. This session focuses on practical business outcomes, using high-level case studies to demonstrate short- and long-term benefits of xAPI. In this session, we will address how xAPI can fit into your learning ecosystem, and how it compares with other learning standards. We'll also discuss the first steps to testing and validating the value of xAPI for your organization.
Learn more at https://www.yetanalytics.com.
The Experience API (xAPI) introduces several design implications for mobile learning that involve user experience (UX) design, interface design, service and system design, organizational design, reporting and analytics design, and instructional design. You’ll hear about the different use cases focusing on commonly anticipated business requirements that will ultimately help determine and prioritize your design objectives. This stage event will be both informative and interactive and will involve audience participation to identify and discuss the potential types of cognitive and performance processes in designing a learning experience using the xAPI.
This slide was used in ISO/IEC JTC1 SC36 Plenary Meeting in June 22, 2015.
Title of this slide is 'Proof of Concept for Learning Analytics Interoperability and subtitle is 'Reference Model based on open source SW'.
How to Plan for Your xAPI Pilot - xAPI Camp at DevLearn 2018 - Yet Analytics Margaret Roth
From an organization-wide executive directive to become more data-driven, a retail corporate L&D team took an internal look at their own data practices. Realizing that they had an overwhelming lack of transparency into their learning initiatives and a great amount of data that had gone unused, the team developed a transformation vision to create a single system of record for learning to enable observability, granularity, and accountability for all team members. The team was committed to the vision of xAPI; however, the data and information they needed in order to make actionable change for their learners was locked away in non-interoperable formats, and they recognized the need to develop a data strategy and implementation plan.
*Originally presented on 10/23/2018 at xAPI Camp during DevLearn 2018 by Allie Tscheulin
Xapi enabled mobile health system with context-awareness & recommendation eng...Jessie Chuang
1. XAPI is a very effective tool in enabling Apps to serve humanity ASAP, because it connects heterogeneous data immediately.
2. XAPI is about people working together. xAPI projects are really across domains collaboration.
3. XAPI is about connecting current technologies, instead of re-inventing wheels.(API’s power)
科技在许多层面改变了我们的生活,这里先从实际的故事来观察学习与培训方式的变化,指出当今人力发展地图上的重要方向,例如社会学习、非正式学习、效能支持、移动学习,接著探讨高管应如何管理与掌握多元样貌的学习行为,去对齐趋近组织目标。利用数据作决策在商业竞争中胜出,人力发展也应该如此。Experience API (xAPI) 就是为这目的而产生的新学习标準。目前 xAPI 实施案最多落在两大类需求: 整合跨系统的历程数据、加强销售绩效。
Technologies have changed many aspects of our society, the landscape of learning and development has been changed a lot. What are important directions on the map? What are social learning, informal learning, performance support & mobile learning and their impacts? With such paradigm shifts, you can’t manage what you don’t measure. Many business competitions are won by data-driven solutions and decisions, so shall talent development management. That led to the introduction of Experience API (xAPI).
For creating open content as a continually ongoing process of refinement, re-distribution, correction, modification, re-arrangement and reuse, better quality of the open content is the result of these possibilities. It's important to make reuse easier. This requires authors to consider visibility and circulation of the published open educational resources(OER).
Plan, research, curate, design, create, publish OER and share it out under an open license, so someone else can discover it, then start with the next step. Maybe it will be improved or expressed in different forms.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
1. Make Learning Big Data Work For You
XAPI Analytics
Jessie Chuang
www.VisualCatch.org
Classroom-aid.com
2. Learning
Planning
LRS
Activity/Agent Profile API
IRS, Quiz service
(e.g.HDtech, H5P)
Integration of Workflow and Data flow *Adaptive design
*Branches/options
Videos or MOOC platform
Mobile Apps
IoT
Wearable
AR
VR ...
Real behavior data sent to
Game/ Gamification/
reporting platforms
3. Why Experience API, xAPI
● Ready data integration no matter where learning happens
● Record any new experience without limitation, driven by
community
● Gain deeper insights into learning process, accumulate
collective intelligence for different stakeholders
● Data reuse is very efficient (data is the new oil)
4.
5. Why xAPI is a
game changer?
Freedom of data flow
Distributed Learning Ecosystem
Unified data structure and document
APIs (state, activity, learner)
Specialized technologies working
together without pain
Freedom of learning design with best
breed of choices & innovations
6. Examples of Developments Enabled by xAPI
Social layer (Both
Actors and Objects
are networked)
Gamification layer
or Behavior Engineering
Technology
Self-paced & adaptive
learning, automated
learning flow across x
Integrated data &
experiences across
platforms / resources
An ecosystem of innovative applications & analytics, and creative uses can grow
out of Data Lakes. (because of data interoperability facilitated by xAPI)
7. Build projects upon
analytics backbone
with
data structure,
Semantic,
visualization
interoperability
Standardized xAPI Profiles/ Recipes /
Vocabularies for basic modules
Standardized visualizations design
Embeddable visualizations to be
monitored along with other data for
goal management
xAPI implementation & statement /
viz design for case-oriented behaviors
8. Hierarchy Design for Effective Visualization & Analysis
We provide standardized xAPI profiles / recipes for standardized visualizations.
Item level:
MCQs
Prompts
Quests
….
Test level:
Summative assess.
Formative assess.
Group polling
….
Raw level:
Videos
Texts
….
LO/Activity
UDL options
Competency
aligned
Authoring Tools
Learning Design Tools
Learning Patterns / Pedagogies / Gamification
Metrics:
Duration / Time Stamp
Response
Completion / Attempts / Usages(e.g. skip)
Metrics:
Duration / Time Stamp / Attempts
Score / Success / Rating
Affective states / Communicate, Collaborate
Metrics:
Patterns vs. Performance
Questions to be answered
Visualization
Communication
Action(able)
Iteration
Rule-based recommender <=>
9. Customized
Dashboards
A dashboard is a visual display of
the most important information
needed
to achieve one or more
objectives
that has been consolidated on a
single computer screen
so it can be
monitored at a glance. (provides an
overview)
-- Stephen Few
Educator / Learning Designer
Learner / Parent
Content Creator / Publisher
HR / Business Line Manager
10. Process Matters!
From content-oriented to experience-oriented design
Data + Design = Behavior Engineering
From fix mindset to growth mindset
Return process data to learners in real time
12. The whole picture =
Training and Learning Architecture(TLA)
● ePortfolio
● Learner modeling
● Machine readable
● Competency
standards
● Knowledge map
● Standard
alignment
● xAPI COP
● Common
vocabulary
● Learning
Design
● Sharing of
metadata &
paradata
● Re-usability
● Semantic analysis
13. We provide standardized xAPI profiles/ recipes for standardized visualizations.
Visualization as a Cognitive Agent, Return Data to Humans in the Loops!
feature list
14. Analytics backbone
+
Customized
dashboards
<= Can be drilled down
View each person’s
time spent
Overall time spent in each activity type for selected group
Overview of Time Spent on All Activities
15. Overview on Timeline
1. Group vs. individual
2. Filtering by activityType, activity id, verb, profile
17. eBook (IEEE Actionable Data Book project)
Interactive chart showing every actions ever happened on each page,
can be drilled down to see who, when, what note/highlight content,
quiz answering records, video watching records
Time spent Heatmap
18. Quiz Answering Records
Encoding visualization with
granular records and standards,
can be drilled down
Help catch patterns, problems
and competencies efficiently
Aggregate all practice data from
different sources
Assessment item analysis
● P-Value
● Discrimination
● Sequence/context analysis ...
22. Correlation Analysis
In this example, learners in 4 quadrants have different needs
To correlate and find factors that matter to your metrics
23. See who’s learned this
-- a peak of social layer on top of different activities
24. Verb cloud
(color matched to the taxonomy level)
Verb taxonomy visualization (points reward higher order verbs)
Hint : Charting Verbs stat. vs. time can see behavior trend
26. Our Current User Base
This project is being implemented in Taipei city, Taiwan. All
teachers and students, about 350,000 in number, in Taipei city
will be using this solution (later other areas will join); the public
department is working towards building an ecosystem (from
collaboration of public departments, schools, vendors, scholars,
and developers) based on xAPI standard and open learning data
as Open Data.
28. Case Studies:
● Leveraging #xAPI to Transform Your Learning Content and Solution
● Across System Learning Environment and Dashboard Design for Education (#xAPI)
● A Teacher with AcrossX Solution Enabled by #xAPI
● Our visualization in IEEE ADB demo
We follow ADL xAPI Vocab. Spec.& best practices: (the first LRS)
● Guidelines for IRI Design and Persistence
● Controlled Vocabulary Considerations for the Experience API (xAPI)
● Relationship Between xAPI Vocabularies, Profiles, and Recipes
● ADL xAPI Vocabulary Spec. & Vocabulary Primer (Feb., 2016)
● Our registry (with help from ADL): https://w3id.org/xapi/acrossx
● Our profiles/recipes: http://wiki.visualcatch.org/en/recipes.html (Gitbook)